A Discrete Interferometric Model for a Layer of a Random Medium: Effects on InSAR Coherence, Power, and Phase

The remote sensing community increasingly demands precise ecosystem monitoring, environmental change detection, and natural resource management, particularly in forestry. Key metrics such as biomass and total area index require accurate estimation, necessitating extensive experiments and reliable scattering models. Recent advances in radar interferometry introduce two essential parameters—interferogram phase and correlation coefficient—containing crucial target information. Understanding their relationship to forest biophysical parameters requires analyzing wave interactions with vegetation particles. This study presents a discrete interferometric model for a random medium layer, establishing the link between radar interferometry and forest biophysical properties. Correlation analysis plays a vital role in estimating one variable based on another, reducing uncertainty in random media. The research introduces a novel modeling approach that enhances theoretical foundations and supports empirical studies in the literature. Bridging theoretical analysis and practical observations, this work enhances the precision and applicability of radar interferometry for vegetation monitoring. The findings contribute to improving remote sensing methodologies and expanding their potential in ecological and environmental research. Ultimately, this study advances the use of interferometric models in extracting critical forest parameters with greater accuracy.